About Me

I am an Assistant Professor of Mechanical and Aerospace Engineering at Oklahoma State University, based at the OSU-Tulsa campus. My research advances trustworthy autonomy for aerospace and robotic systems, with an emphasis on planning and control under uncertainty and limited computational resources.

A central part of my work develops optimal control, stochastic control, and convex optimization methods. I investigate how to jointly design trajectories and feedback policies, shape uncertainty through covariance control, and make constrained optimization practical for autonomous decision-making. I also develop machine learning-enabled methods, including physics-informed neural networks and reinforcement-learning-enhanced control, to make effective use of data and improve performance under model mismatch.

My group pursues model-based, learning-enabled, and hybrid approaches according to the needs of each problem. Applications span spacecraft guidance and control, launch vehicles, UAV navigation, advanced air mobility, robotic systems, and space domain awareness. At OSU-Tulsa, I am building a program that connects mathematical research and computational algorithms with verification and experimental validation. Through the aerospace ecosystem, including OAIRE and the LaunchPad Center for Advanced Air Mobility, I am interested in taking these methods from simulation to laboratory experiments and flight testing.

I received my Ph.D. in Aeronautical and Space Engineering from Sapienza University of Rome in 2022. My academic journey also included postdoctoral appointments at Sapienza and the University of Arizona, as well as a visiting research period at the University of Tennessee. Across these experiences, I worked on topics ranging from convex and stochastic optimal control to physics-informed learning, reinforcement-learning-enhanced control, and machine learning for space domain awareness. These experiences shaped a research program in which advances in mathematical control and optimization, learning-based methods, and their integration each contribute to autonomous systems.

Research, teaching, and mentorship are deeply connected in how I view an academic career. I am committed to creating learning and research environments where students are encouraged to ask difficult questions, test new ideas, learn from setbacks, and gradually develop into independent, creative, and ethical engineers and researchers. I particularly value the collaborative nature of research and the opportunity for mentors, peers, and students to learn from one another.